Development of a Generic decision support system based on multi-Objective Optimisation for Green supply chain network design (GOOG). Issue 7 (7th September 2015)

• Record Type: Journal Article
• Authors: Boonsothonsatit, Kanda; Kara, Sami; Ibbotson, Suphunnika; Kayis, Berman
• Is Part Of: Journal of manufacturing technology management. Volume 26:Issue 7(2015)
• Page Start: 1069
• Page End: 1084
• Publication Date: 2015-09-07
• Subjects: Decision support systems -- Product life cycle -- Sustainable production -- Partnership -- Supply chain management -- Manufacturing operations; Computer integrated manufacturing systems -- Periodicals; Manufacturing processes -- Automation -- Periodicals; Industrial engineering -- Periodicals; Manufacturing industries -- Management -- Periodicals; 670.5
• DOI: 10.1108/JMTM-10-2012-0102
• Languages: English
• ISSNs: 1741-038X

Abstract

Abstract : Purpose: – The purpose of this paper is to propose a Generic decision support system which is based on multi-Objective Optimisation for Green supply chain network design (GOOG). It aims to support decision makers to design their supply chain networks using three key objectives: the lowest cost and environmental impact and the shortest lead time by incorporating the decision maker's inputs. Design/methodology/approach: – GOOG aims to suggest the best-fitted parameters for supply chain partners and manufacturing plant locations, their order allocations, and appropriate transportation modes and lot-sizes for cradle-to-gate. It integrates Fuzzy Goal Programming and weighted max-min operator for trade-off conflicting objectives and overcome fuzziness in specifying target values of individual objectives. It is solved using exact algorithm and validated using an industrial case study. Findings: – The comparative analysis between actual, three single-objective, and multi-objective decisions showed that GOOG is capable to optimising three objectives namely cost, lead time, and environmental impact. Research limitations/implications: – Further, GOOG requires validation for different supply chain scenarios and manufacturing strategic decisions. It can improve by including multi-echelon supply chain networks, entire life cycle and relevant environmental legislations. Practical implications: – GOOG helps the decision makers to configuring those supply chain parameters whilst minimising those three objectives. Social implications: – Companies can use GOOG as a tool to strategically select their supply chain that reduces their footprint and stop rebound effect which imposes significant impact to the society. Originality/value: – GOOG includes overlooked in the previous study in order to achieve the objectives set. It is flexible for the decision makers to change the relative weightings of the inputs for those contradicting objectives.